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and I call a Tom knife on vice-presidentclinical operations from metamericgenetics and he will talk aboutoptimizing efficiency of a clinicalmolecular diagnostic essay using anintegrated fully automated imageanalysis workflow yes that's fine goodmorning everyone so really I'm going to

focus this morning on much more than theinfrastructure that we put in place inorder to operationalize rather compleximage based assay in a CLIA setting alittle bit of background about the thetest itself though that we put togetherso our research team had identified aset of protein biomarkers that hadrelevance when looking at a prostatebiopsy for being able to predict whetherthe pathology and the prostate itselfwas either either aggressive or of afavorable type and then we use that setof biomarkers in order to put togetheran assay we chose immunofluorescence forthe reasons that can discussed yesterdayit allowed us to multiplex and it alsohas a much better dynamic range I willsay that there are some caveats to usingimmunofluorescence as well when you goto look at instrumentation out therethere you don't have nearly the choicesthat you do with bright field so theyare more limited expensive not asrobustly tested out in the clinicalsetting and throughput can be an issueas well so we had a lot of learningalong the way in order to operationalizethis so what we did then was a we wecan't put sixty five markers on like GEis doing but we're simultaneouslymeasuring six different fluorophores sowe have add a p-channelwe have two channels that we use fordistinguishable tissue mask markers andthen we have three channels where wemeasure the biomarkers themselves sowe're not able to put our entire essayon a single slide right now we're usingfour slides so we are capturing theseimages with the CRI vectra and this justshows what a spectral library would looklike we have put about a thousandsamples through our operations at thispoint most recently a validation setthat's just shy of 400 biopsy cases soif you're going to put a commercialassay together it obviously has to havea value proposition associated with itand in this case and in the UnitedStates in particular the majority ofprostate cancers that are diagnoseddiagnosed or early stage so Gleason 3plus 3 or 3 plus 4 perhaps in theneighborhood of eighty percent of thosepatients actually go on to have radicaltherapy in this country although themajority of those cases are actuallyindolent and really could be safelyfollowed with a watchful waiting type ofcohort and so you know we're hoping thatwe can guide patients into these activesurveillance programs now as theresearch team was developing the assayitself over the past just over a yearnow what we were doing on the CLIAlaboratory side was you know reallylooking at how to operationalize thisand get the appropriate robustness andthroughput that we need in order to dothis commercially so in this firstsentence here there's really a lot inthis okay we were looking to take thiscomplex assay and fully automate it sosample identification from thelaboratory information system at thebeginning back into the laboratoryinformation system at the tail end allof the image handlewhich is a complex coming out of the CRIvectra that instrument is not a wholeslide scanner so we have multiple fieldsof view associated with the case loadingtaking these files loading them into thedefendants environment performing anautomated analysis for ROIidentification as well as biomarkerquantitation and then sending all thatinformation automatically back into thelis where a all of those biomarkervalues get combined for a risk scoredetermination and we set up somebenchmarking here that I don't have anoutcome measurement of that yet butreally what we're looking to do is makeit make this as automated as possibleevery manual intervention is anopportunity for an error to occur or forjudgment to have to be placed so we'relooking to take the pathologist from thefront end of this process and move himor her to the back end so that thepathologist is reviewing what should bereproducible data rather thanintervening and selecting the areaswhere that data comes from so thestarting point or the input into theprocess is the laboratory informationsystem and we chose orchard and whatI'll show here first is the orchardpathology this is an anatomic pathologysystem what this allows us to do isorder a case for for a patient and shownhere that'd be ordering our prostatebiopsy test or pbx so internally thenthat explodes this out to the fourprotocols that will be used on fourslide so pbx ABC and D prints the barcode for the slides and that is a single2d barcode that we use it has a uniqueslide identifier and it has a protocolembedded into it we have the patientslides as well as we engineered our owncontrol slideswith quantitative levels for all of thebiomarkers that we're using and thesebarcodes are then read by the autostainer to apply the appropriateprotocol now a big reason why weselected orchard is not only do theyhave the anatomic pathology componentbut they also then have the clinicalpathology component called the orchardharvest these aren't completelyintegrated systems but they do at leasttalk to each other quite well and sowhen we place this single order for thepbx this cascades into the clinicalpathology system and places the ordersfor all of the quantitative measurementsthat we expect to get back after theimage analysis when the slides come outof the auto stainer they then go intothe CRI vector where the images arecaptured the CRI vector 0 also has abarcode scanner it reads the same 2dbarcode and it then embeds that uniqueidentifier in the protocol into theimage files all right everything elsehappens coming out of the CRI vectra ina completely hands-off manner so the CRIvectra scans and creates thesehigh-power fields for each field of viewat the end of that process it creates ascan summary file so sitting on thelocal PC where these images areinitially created we have a script thatwatches for the scan summary when thatfile comes in we error check it we makesure that all of the files are thereeverything is appropriately labeled andthen we transfer all those files ontothe server make sure that process iscompleted error-free and then pass theticket over to the server where anotherscript picks up the process the CRIvector images are stored as theseproprietary image cubes so weautomatically apply the unmixingstrategy from CRI vectorand the spectral library separate thoseinto the component files those come outas floating-point tips which are alsonot directly importable into thedefendants environment but weautomatically then separate thosefloating point Tiff's into individualtiff layer files and once those arecompleted everything is error checked wethen bring in the creative workspaceautomatically bring in the customizedimport the appropriate rule set and welaunch the analysis so internally withinthe defendants environment then whenthis analysis is launched we aresegmenting our epithelium our nucleibecause we're using more than one tissuemask we then apply that in order toseparate out the malignant from thebenign regents from the stroma hereclassifying the nuclei here the glandsthemselves where the red represents themalignant areas the green represents thebenign areas and we do classify areasthat have uncertainty as well not easilyclassified as either we can excludethose from the analysis but at the sametime we can keep track of you know thata parameter of our confidence onanalyzing this slide as how large thesesuspicious areas are ultimately we endup with the subcellular region ofinterest being defined where we canmeasure our specific biomarkers we havea fairly robust system for evaluatingimage QC so obvious artifacts such asshown here can be excluded and this thisis one of our cell line controls at thetop here and this is a tissue sectionhere from one of the biopsies but we caneliminate specific areas from images orwe also you know have a threshold whereif the artifact encompasses too large ofan area of that particular image we dropthe image altogether we also do measuresome biomarkers and at the end of theprocess look across all ofthe images to see if it meets ourbiomarker driven quality parameters youknow that this is you know there are noperfect markers for doing this but we'repretty confident that we have a good wayof detecting samples that should beexcluded and that we should say this isnot an interpretable sample havingnothing to do with the assay itself butsomething within the the quality of thesample an example as we were bringingsamples in for the cohort one of theproviders unbeknownst to us was using afairly aggressive microwave technologyyou could look at that tissue and lookat an H&E everything about it lookedfine but all of the biomarker valueswere severely suppressed because theywere denatured proteins and we're ableto pick that up by looking at somequality markers so continuing on againin an automated fashion the export fromthe defendants well we you know have thesort of routine csv files that are goingout containing all of the projectinformation that was very important forthe development phases of this but whatwe've done is we've taken any of thesort of post opinions processing and reinternalize that back into thedefendants environment so really from aclinical standpoint what we have comingout are discrete values within thedefendants itself we're parsing thosefile names then and pulling out theunique slide identifier pulling out theprotocol so we know which biomarkersthese are what test it corresponds toand so we drop those as discrete valuesright back into the Harvard lis systemwhere it picks them up and puts theminto the patient file also creating astack of JPEG images that show all ofthe segmentation that took place andwhat this allows is to put the thepathologist again at the back end wherewe created an interface gives a verysimple way to just flip throughlook at the segmentation and make surethat what's being analyzed isappropriate and there's an opportunityhere to intervene and drop images andreprocess so the data itself then thequal quality control slides are analyzedin the same way as the the patientsamples so these are reproducible celllines that we grow but we fix them weembed them in paraffin take them throughthe whole process along with a batch ofpatient samples and then that data getsparsed within de Finian's and discretelysent out into the laboratory informationsystem as well so each individual valuethen comes in and gets we exploit theclinical pathology tools in the harvestdatabase so that we can place these onLevi Jennings charts and follow thequality controls the quality control fora given batch is linked then to thepatient results in that batch so theseresults can't be accepted until thequality control has been reviewed andaccepted that then releases the patientdata the patient data then once it'sbeen released within the database itallows for a final rule set in thelaboratory information system B to betriggered where all of the biomarkervalues then are brought together inorder to calculate a risk score thisdepending on the final risk score thenthat grabs the appropriate graphics etcfor a predefined report and that's readyto be reviewed and go out the doorso in conclusion we've really you knowsuccessfully it incorporated this trulyautomated quantitative image analysisplatform into a CLIA laboratory settingso we've you don't use multipleoff-the-shelf components as well as somecustomized scripting in house in orderto put this system all together we'recompleting a validation study right nowreally the main thing we're looking atis from a clinical outcomes standpointyou know do we meet the bar of successbut as a secondary evaluation we'relooking at the efficiency as well againin operationalizing this really wantedto be as hands off of the system aspossible we recently purchased imageminor as well which we think will be auseful tool for evaluating all of theoutliers and help us to fine-tune therule set you know I'd love to have it beimage and viewing free and to have greatconfidence that we can put this convertthese images all the way into data havea report ready and really be confidentthat that data was all extracted fromappropriate images and finally someacknowledgments internally the the teammet at mark both in our D as well as inthe CLIA laboratory several people fromde Finian's has helped out in thisproject both from rural set developmentto also assisting with the scripting andwe really needed command-line scripts inorder to embed everything into this andmake an automated process also needed toget some command-line scripting from CRIvectra work closely with orchardsoftware this was not an off-the-shelfapplication coming from orchardespecially some of the complexity andputting this batching a process togetherto link the patient samples to theseslide unique slides that we put togetherand then I worked with aa software programmer as well for someof it put all of this scripting togetherso thank you and we're saving questionsthen for the the panel or well wethere's one yet yes okay Jonah said wesave question thank you very much allright